[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127576-en":3,"doc-seo-127576-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127576,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Improving resilience of sensors in planetary exploration using data-driven models","Improving resilience of sensor systems in space exploration addresses harsh environmental exposure where flying debris and Dust Devils can partially damage rover/lander sensors. The work proposes data-driven methods for complex multi-variable sensors using an inverse algorithm based on calibration data. The approach spans laboratory calibration and inverse-algorithm design, model training at the intended location, and post-detection error reduction when partial damage occurs. Tests use TWINS wind-sensor intermediate outputs, comparing machine learning and deep learning.","PAPER • OPEN ACCESS  \nImproving resilience of sensors in planetary exploration using data-driven models  \nTo cite this article: Dileep Kumar et al 2023 Mach. Learn. : Sci. Technol. 4 035041  \nView the article online for updates and enhancements.  \nYou may also like  \n-How does the control logic influence the establishment of a data-driven chiller model?  \nShunian Qiu, Zhenhai Li, Ruikai He et al.  \n-Efficient system-level simulations of thermal wind sensors considering environmental factors  \nYun-Quan Wang, Zai-Fa Zhou, ZhenXiang Yi et al.  \n-Data-Driven Decision Making in Battery Technology – How to Compete in Global Battery Industry?  \nAndré Hemmelder  \nThis content was downloaded from IP address [84.88.66.150](84.88.66.150) on 13/01/2025 at 11:49  \n Mach. Learn.: Sci. Technol. 4 (2023) 035041 [https://doi.org/10.1088/2632-2153/acefaa](https://doi.org/10.1088/2632-2153/acefaa)  \nOPEN ACCESS  \nRECEIVED  \n12 May 2023  \nREVISED  \n17 July 2023  \nACCEPTED FOR PUBLICATION  \n10 August 2023  \nPUBLISHED  \n4 September 2023  \nOriginal content from this work may be used under the terms of the  \nCreative Commons Attribution 4 .0 licence.  \nAny further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \nPAPER  \nImproving resilience of sensors in planetary exploration using data-driven models  \nDileep Kumar1􀁂, Manuel Dominguez-Pumar1, ∗􀁂, Elisa Sayrol-Clols2􀁂, Josefina Torres3􀁂 , Mercedes Marín3􀁂, Javier Gómez-Elvira3􀁂, Luis Mora3􀁂, Sara Navarro3􀁂  \nand Jose Rodríguez-Manfredi3, ∗􀁂  \n1 Universitat Politècnica de Catalunya (UPC), Barcelona, Spain  \n2 TecnoCampus, Universitat Pompeu Fabra (UPF), Mataró, Spain  \n3 Centro de Astrobiología (INTA-CSIC), Madrid, Spain  \n∗ Authors to whom any correspondence should be addressed.  \n[E-mail:](E-mail: manuel.dominguez@upc.edu and manfredi@cab.inta-csic.es)[ manuel.dominguez@upc.edu](E-mail: manuel.dominguez@upc.edu and manfredi@cab.inta-csic.es)[ and](E-mail: manuel.dominguez@upc.edu and manfredi@cab.inta-csic.es)[ manfredi@cab.inta-csic.es](E-mail: manuel.dominguez@upc.edu and manfredi@cab.inta-csic.es)  \nKeywords: space sensor systems, wind sensor, machine learning, deep learning, soft sensor  \nAbstract  \nImproving the resilience of sensor systems in space exploration is a key objective since the environmental conditions to which they are exposed are very harsh. For example, it is known that the presence of flying debris and Dust Devils on the Martian surface can partially damage sensors present in rovers/landers. The objective of this work is to show how data-driven methods can improve sensor resilience, particularly in the case of complex sensors, with multiple intermediate variables, feeding an inverse algorithm (IA) based on calibration data. The method considers three phases: an initial phase in which the sensor is calibrated in the laboratory and an IA is designed; a second phase, in which the sensor is placed at its intended location and sensor data is used to train data-driven model; and a third phase, once the model has been trained and partial damage is detected, in which the data-driven algorithm is reducing errors. The proposed method is tested with the intermediate data of the wind sensor of the TWINS instrument (NASA InSight mission), consisting of two booms placed on the deck of the lander, and three boards per boom. Wind speed and angle are recovered from the intermediate variables provided by the sensor and predicted by the proposed method. A comparative analysis of various data-driven methods including machine learning and deep learning (DL) methods is carried out for the proposed research. It is shown that even a simple method such as k-nearest neighbor is capable of successfully recovering missing data of a board compared to complex DL models. Depending on the selected missing board, errors are reduced by a factor between 2.43 and 4.78, for horizontal velocity; and by a factor between 1.74 and 4.71, for angle","cbCaikqhTmaL5HQ3","https://ap.wps.com/l/cbCaikqhTmaL5HQ3","pdf",2791082,3,1,19,"English","en",105,"# Abstract\n# Introduction\n## Sensor reliability and resilience in space exploration\n## Motivation from Martian wind sensing and partial damage","[{\"question\":\"Why is improving sensor resilience critical for planetary exploration?\",\"answer\":\"Space missions face harsh environments where missing or damaged sensor data can strongly impact scientific output, and repairs are often impractical.\"},{\"question\":\"What data-driven method does the paper propose to improve sensor resilience?\",\"answer\":\"It combines an inverse algorithm based on calibration data with a three-phase workflow: laboratory calibration and IA design, training at the target location, and error reduction after detecting partial damage.\"},{\"question\":\"How is the proposed method evaluated in the study?\",\"answer\":\"The method is tested using intermediate outputs from the TWINS instrument wind sensor on NASA InSight, recovering wind speed and angle and comparing k-nearest neighbors and other machine/deep learning approaches.\"}]","Improving resilience of sensors in planetary exploration using data-driven models | 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is improving sensor resilience critical for planetary exploration?","Question",{"text":76,"@type":77},"Space missions face harsh environments where missing or damaged sensor data can strongly impact scientific output, and repairs are often impractical.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data-driven method does the paper propose to improve sensor resilience?",{"text":81,"@type":77},"It combines an inverse algorithm based on calibration data with a three-phase workflow: laboratory calibration and IA design, training at the target location, and error reduction after detecting partial damage.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the proposed method evaluated in the study?",{"text":85,"@type":77},"The method is tested using intermediate outputs from the TWINS instrument wind sensor on NASA InSight, recovering wind speed and angle and comparing k-nearest neighbors and other machine/deep learning 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